{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql\n",
    "%sql postgresql://postgres:t}LQ7ZL]3$x~I8ye@35.225.163.235:5432/postgres\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3624423/1568155252.py:40: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, conn, params=('%s1_ablation%', '%MATH500_accuracy%'))  # Ensure both values are passed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>weights_location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.610</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_diversity_sampling_1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.724</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.788</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_diversity_sampling_9k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.708</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_1k_10e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.704</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_3k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.788</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_diversity_sampling_27k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.586</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_9k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.798</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_27k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.740</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_diversity_sampling_3k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.714</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_9k_10e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.668</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_1k_5e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.670</td>\n",
       "      <td>mlfoundations-dev/qwen_s1ablation_length_filter_9k_5e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    score                                          weights_location\n",
       "0   0.610   mlfoundations-dev/qwen_s1ablation_diversity_sampling_1k\n",
       "1   0.724        mlfoundations-dev/qwen_s1ablation_length_filter_1k\n",
       "2   0.788   mlfoundations-dev/qwen_s1ablation_diversity_sampling_9k\n",
       "3   0.708    mlfoundations-dev/qwen_s1ablation_length_filter_1k_10e\n",
       "4   0.704        mlfoundations-dev/qwen_s1ablation_length_filter_3k\n",
       "5   0.788  mlfoundations-dev/qwen_s1ablation_diversity_sampling_27k\n",
       "6   0.586        mlfoundations-dev/qwen_s1ablation_length_filter_9k\n",
       "7   0.798       mlfoundations-dev/qwen_s1ablation_length_filter_27k\n",
       "8   0.740   mlfoundations-dev/qwen_s1ablation_diversity_sampling_3k\n",
       "9   0.714    mlfoundations-dev/qwen_s1ablation_length_filter_9k_10e\n",
       "10  0.668     mlfoundations-dev/qwen_s1ablation_length_filter_1k_5e\n",
       "11  0.670     mlfoundations-dev/qwen_s1ablation_length_filter_9k_5e"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import psycopg2\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "pd.set_option('display.max_colwidth', None)  # Show full text in cells\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "conn = psycopg2.connect(\n",
    "    host=\"35.225.163.235\",\n",
    "    dbname=\"postgres\",\n",
    "    user=\"postgres\",\n",
    "    password=\"t}LQ7ZL]3$x~I8ye\",\n",
    "    port=5432\n",
    ")\n",
    "\n",
    "cur = conn.cursor()\n",
    "\n",
    "# SQL query to join evalresults with models & evalsettings\n",
    "query = \"\"\"\n",
    "SELECT \n",
    "    e.*, \n",
    "    m.name AS model_name,\n",
    "    m.weights_location,\n",
    "    s.name AS eval_name, \n",
    "    d.name AS dataset_name,\n",
    "    s.name AS evalsettings_name,  -- Added evalsettings name\n",
    "    s.parameters->>'annotator_model' AS annotator_model  -- Extract annotator_model from JSON\n",
    "FROM evalresults e\n",
    "JOIN models m ON e.model_id = m.id\n",
    "JOIN evalsettings s ON e.eval_setting_id = s.id\n",
    "JOIN datasets d ON e.dataset_id = d.id\n",
    "WHERE m.name ILIKE %s\n",
    "AND s.name ILIKE %s\n",
    "AND s.parameters->>'annotator_model' = 'auto';  -- Filter where annotator_model is 'auto'\n",
    "\"\"\"\n",
    "\n",
    "# Correct `params` with TWO values\n",
    "df = pd.read_sql(query, conn, params=('%s1_ablation%', '%MATH500_accuracy%'))  # Ensure both values are passed\n",
    "\n",
    "# 🔹 Keep only `score` and `weights_location`\n",
    "df_filtered = df[['score', 'weights_location']]\n",
    "\n",
    "# Display results\n",
    "display(df_filtered)\n",
    "# Close connection\n",
    "conn.close()\n",
    "\n",
    "\n",
    "#display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MATH500')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diversity = np.array([\n",
    "    [1000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_diversity_sampling_1k\", 'score'].values[0]],\n",
    "    [3000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_diversity_sampling_3k\", 'score'].values[0]],\n",
    "    [9000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_diversity_sampling_9k\", 'score'].values[0]],\n",
    "    [27000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_diversity_sampling_27k\", 'score'].values[0]]\n",
    "])\n",
    "\n",
    "\n",
    "# plot the data with logarithmic x-axis\n",
    "plt.figure()\n",
    "plt.plot(diversity[:,0], diversity[:,1], marker='o')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Number of training sequences')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Diversity ablation')\n",
    "\n",
    "\n",
    "length = np.array([\n",
    "     [1000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_9k\", 'score'].values[0]],\n",
    "#    [1000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_1k\", 'score'].values[0]],\n",
    "    [3000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_3k\", 'score'].values[0]],\n",
    "#    [9000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_9k\", 'score'].values[0]],\n",
    "    [27000, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_27k\", 'score'].values[0]]\n",
    "])\n",
    "\n",
    "\n",
    "# plot the data with logarithmic x-axis\n",
    "plt.figure()\n",
    "plt.plot(length[:,0], length[:,1], marker='o')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Number of training sequences')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Length filter ablation')\n",
    "\n",
    "\n",
    "#epochs1k = np.array([\n",
    "#    [5, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_1k_5e\", 'score'].values[0]],\n",
    "#    [10, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_1k_10e\", 'score'].values[0]],\n",
    "#    [15, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_1k\", 'score'].values[0]],\n",
    "#])\n",
    "\n",
    "epochs1k = np.array([\n",
    "    [3, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_9k\", 'score'].values[0]],\n",
    "    [5, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_9k_5e\", 'score'].values[0]],\n",
    "    [10, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_9k_10e\", 'score'].values[0]],\n",
    "    [15, df.loc[df['weights_location'] == \"mlfoundations-dev/qwen_s1ablation_length_filter_1k\", 'score'].values[0]],\n",
    "])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(epochs1k[:,0], epochs1k[:,1], marker='o',label='1k training examples')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('MATH500')\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (my_env)",
   "language": "python",
   "name": "my_env"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
